Risk factors of developing critical conditions in Iranian patients with COVID-19

COVID-19 due to novel Coronavirus was first reported in Wuhan, China. Nowadays, the Islamic Republic of Iran stands among countries with high COVID-19 prevalence and high burden of disease. Since the medical resources are limited, we aimed to identify the risk factors for patients developing critical conditions. This can help to improve resource management and treatment outcomes. In this retrospective study, we included 12,677 patients who were from 26 hospitals, supervised by Tehran University of Medical Sciences with signs and symptoms of COVID-19, until April 12. University integrated IT system was adopted to collect the data. We performed Logistic regression to evaluate the association between death in COVID-19 positive patients and other variables. Cough, respiratory distress and fever were the most common symptoms in our patients, respectively. Cancer, chronic lung diseases and chronic neurologic diseases were the strongest risk factors for death in COVID-19 patients. © 202

Seroprevalence of SARS-CoV-2 in Guilan Province, Iran, April 2020

We determined the seroprevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in an affected area in northern Iran in April 2020. Antibodies to SARS-CoV-2 were detected in 528 persons by using rapid tests. Adjusted prevalence of SARS-CoV-2 seropositivity was 22.2 (95 CI 16.4-28.5). © 2021 Centers for Disease Control and Prevention (CDC). All rights reserved

The causal effect and impact of reproductive factors on breast cancer using super learner and targeted maximum likelihood estimation: a case-control study in Fars Province, Iran

Objectives: The relationship between reproductive factors and breast cancer (BC) risk has been investigated in previous studies. Considering the discrepancies in the results, the aim of this study was to estimate the causal effect of reproductive factors on BC risk in a case-control study using the double robust approach of targeted maximum likelihood estimation. Methods: This is a causal reanalysis of a case-control study done between 2005 and 2008 in Shiraz, Iran, in which 787 confirmed BC cases and 928 controls were enrolled. Targeted maximum likelihood estimation along with super Learner were used to analyze the data, and risk ratio (RR), risk difference (RD), andpopulation attributable fraction (PAF) were reported. Results: Our findings did not support parity and age at the first pregnancy as risk factors for BC. The risk of BC was higher among postmenopausal women (RR = 3.3, 95 confidence interval (CI) = (2.3, 4.6)), women with the age at first marriage �20 years (RR = 1.6, 95 CI = (1.3, 2.1)), and the history of oral contraceptive (OC) use (RR = 1.6, 95 CI = (1.3, 2.1)) or breastfeeding duration �60 months (RR = 1.8, 95 CI = (1.3, 2.5)). The PAF for menopause status, breastfeeding duration, and OC use were 40.3 (95 CI = 39.5, 40.6), 27.3 (95 CI = 23.1, 30.8) and 24.4 (95 CI = 10.5, 35.5), respectively. Conclusions: Postmenopausal women, and women with a higher age at first marriage, shorter duration of breastfeeding, and history of OC use are at the higher risk of BC. © 2021, The Author(s)

MEMS Gyroscope Raw Data Noise Reduction Using Fading Memory Filter

553-558Nowadays, MEMS sensors are widely used in systems such as autonomous vehicles, but they still suffer from high stochastic errors such as Angle random walk (ARW) noise, which causes failure in real-signals and produces an error in the position and attitude of mobile systems. So far, many filters are developed to reduce the amount of noise in the output of the MEMS sensors. The computational overhead, the rate of noise reduction, and the phase-delay of the filter are the most important characteristics of choosing a suitable filter. In this paper, a low pass filter based on the alpha-beta filter with a very low computational overhead is proposed to reduce the amount of noise. In order to find the optimal filter gain, the improvement in the positioning is selected as a criterion, which is a tradeoff between the amount of noise reduction and the phase delay of the filtered signal. In this work, the KITTI database is used to evaluate the proposed filter. The results show that the proposed filter reduces the sensor’s noise and improves the positioning of the moving car, significantly

The effect of smoking on latent hazard classes of metabolic syndrome using latent class causal analysis method in the Iranian population

Background: The prevalence of metabolic syndrome is increasing worldwide. Clinical guidelines consider metabolic syndrome as an all or none medical condition. One proposed method for classifying metabolic syndrome is latent class analysis (LCA). One approach to causal inference in LCA is using propensity score (PS) methods. The aim of this study was to investigate the causal effect of smoking on latent hazard classes of metabolic syndrome using the method of latent class causal analysis. Methods: In this study, we used data from the Tehran Lipid and Glucose Cohort Study (TLGS). 4857 participants aged over 20 years with complete information on exposure (smoking) and confounders in the third phase (2005–2008) were included. Metabolic syndrome was evaluated as outcome and latent variable in LCA in the data of the fifth phase (2014–2015). The step-by-step procedure for conducting causal inference in LCA included: (1) PS estimation and evaluation of overlap, (2) calculation of inverse probability-of-treatment weighting (IPTW), (3) PS matching, (4) evaluating balance of confounding variables between exposure groups, and (5) conducting LCA using the weighted or matched data set. Results: Based on the results of IPTW which compared the low, medium and high risk classes of metabolic syndrome (compared to a class without metabolic syndrome), no association was found between smoking and the metabolic syndrome latent classes. PS matching which compared low and moderate risk classes compared to class without metabolic syndrome, showed that smoking increases the probability of being in the low-risk class of metabolic syndrome (OR: 2.19; 95% CI: 1.32, 3.63). In the unadjusted analysis, smoking increased the chances of being in the low-risk (OR: 1.45; 95% CI: 1.01, 2.08) and moderate-risk (OR: 1.68; 95% CI: 1.18, 2.40) classes of metabolic syndrome compared to the class without metabolic syndrome. Conclusions: Based on the results, the causal effect of smoking on latent hazard classes of metabolic syndrome can be different based on the type of PS method. In adjusted analysis, no relationship was observed between smoking and moderate-risk and high-risk classes of metabolic syndrome.Medicine, Faculty ofNon UBCOphthalmology and Visual Sciences, Department ofReviewedFacultyResearche

Effect of alcohol consumption on breast cancer: probabilistic bias analysis for adjustment of exposure misclassification bias and confounders

PurposeThis study was conducted to evaluate the effect of alcohol consumption on breast cancer, adjusting for alcohol consumption misclassification bias and confounders.MethodsThis was a case-control study of 932 women with breast cancer and 1000 healthy control. Using probabilistic bias analysis method, the association between alcohol consumption and breast cancer was adjusted for the misclassification bias of alcohol consumption as well as a minimally sufficient set of adjustment of confounders derived from a causal directed acyclic graph. Population attributable fraction was estimated using the Miettinen's Formula.ResultsBased on the conventional logistic regression model, the odds ratio estimate between alcohol consumption and breast cancer was 1.05 (95 CI: 0.57, 1.91). However, the adjusted estimates of odds ratio based on the probabilistic bias analysis ranged from 1.82 to 2.29 for non-differential and from 1.93 to 5.67 for differential misclassification. Population attributable fraction ranged from 1.51 to 2.57 using non-differential bias analysis and 1.54-3.56 based on differential bias analysis.ConclusionA marked measurement error was in self-reported alcohol consumption so after correcting misclassification bias, no evidence against independence between alcohol consumption and breast cancer changed to a substantial positive association